Mapping invasive Opuntia stricta in Kenya’s Drylands using explainable machine learning with time-series remote sensing and geographic context

Abstract

Opuntia stricta is a globally widespread invasive species that degrades dryland ecosystems and threatens pastoral livelihoods. Accurate, high-resolution distribution maps are essential for effective management, but its spectral similarity to native vegetation complicates remote sensing-based classification. We developed an interpretable Random Forest model, incorporating SHapley Additive exPlanations (SHAP), to map Opuntia stricta and co-occurring land cover types at 10  m resolution across heterogeneous arid and semi-arid lands in Laikipia County, Kenya. The model integrated monthly Sentinel-2 imagery, climate, topographic, landscape structural, and anthropogenic factors. Field surveys were combined with manual labeling using Google Maps and Street View to address annotation gaps in remote areas. Grid-based spatial blocking at multiple scales was used to reduce spatial autocorrelation and assess generalizability. The multi-temporal model achieved 0.91 overall accuracy and an F1-score of 0.92 for Opuntia stricta on the spatial validation set (100 m grid), and 0.86 accuracy with an F1-score of 0.85 on the independent test set, substantially outperforming single-month models (accuracy: 0.62–0.79; F1: 0.67–0.82), with February identified as the most informative single-time window. SHAP analysis identified July precipitation, population density and nighttime land surface temperatures as top predictors, linking invasion patterns to dry-season aridity, wet-season rainfall, and warm night conditions, underscoring the role of climate seasonality and human activity in shaping detectability and distribution. Invasion hotspots were concentrated near Dol-Dol and in degraded group ranches, with lower levels on private ranches and conservancies. Our findings highlight the potential of multi-temporal, context-integrated remote sensing for targeted invasive species management in dryland ecosystems.